Computer network security early warning device and use method thereof

Abstract

The invention provides a computer network security early warning device and a use method thereof. The computer network security early warning device comprises a data acquisition module, a preprocessing and cleaning module, an intelligent analysis processing module, a zero-trust access control module, an early warning response module, an automatic response and isolation module and a quantum security encryption module. By introducing intelligent analysis technologies such as machine learning and deep learning, the method can automatically learn and adapt to complex and changeable network attack modes, the flexibility and adaptability of the early warning device are improved, and novel and unknown attack means are effectively coped with; by integrating a big data analysis technology and an efficient algorithm model, the speed and precision of data processing and analysis are remarkably enhanced, more comprehensive and more detailed network environment information is obtained through a multi-source data acquisition and deep mining technology, various types of security threats are accurately identified in combination with an intelligent analysis algorithm, and the security threats are effectively identified. Therefore, the comprehensiveness and accuracy of early warning are greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of computer network security, and in particular to a computer network security early warning device and a use method thereof. Background Art

[0002] With the rapid development and widespread application of information technology, computer networks have become an indispensable infrastructure in modern society. However, at the same time, network security threats are becoming increasingly severe. Frequent security incidents such as hacker attacks, malware, data leaks, and online fraud have brought huge challenges to the information security of individuals, enterprises, and countries. Traditional network security protection methods, such as firewalls and intrusion detection systems, are unable to cope with increasingly complex and changeable network attacks.

[0003] In order to effectively deal with network security threats and protect the security of information assets, network security early warning technology has emerged. Network security early warning technology is a technology that predicts and discovers security events in the network. It can warn of possible security threats in the network in advance, so as to take security protection measures in time. By real-time monitoring of network traffic, analyzing log data, identifying abnormal behaviors, etc., network security early warning technology can timely discover potential security risks and notify relevant personnel to deal with them. Although network security early warning technology has made certain progress, there are still some problems:

[0004] First, traditional early warning systems often rely on fixed rules and templates to identify attack behaviors, which makes it difficult to cope with complex and changing network attack patterns;

[0005] Second, the existing technology still needs to be improved in terms of data processing, analysis and response speed, and cannot meet the requirements of real-time, accuracy and comprehensiveness;

[0006] 3. Existing safety warning devices usually consist of temperature sensors and alarms, and their functions are limited to monitoring. When the temperature is detected to be too high, effective measures cannot be taken immediately to intervene, thus failing to effectively alleviate the problem in real time. In addition, the heat dissipation performance of computer network control devices depends largely on the smoothness of the dustproof net. However, the safety warning devices in current technology lack an automatic response mechanism when the dustproof net is blocked, and cannot meet the needs of practical applications.

[0007] To this end, a computer network security early warning device and a method for using the same are proposed. Summary of the invention

[0008] In view of this, the embodiments of the present invention hope to provide a computer network security early warning device and a method of using the same, so as to solve or alleviate the technical problems existing in the prior art and at least provide one of the beneficial options.

[0009] In order to solve the above technical problems, a technical solution adopted in this application is: a computer network security early warning device, including a data acquisition module, a preprocessing and cleaning module, an intelligent analysis and processing module, a zero-trust access control module, an early warning response module, an automated response and isolation module, a quantum security encryption module, a cloud collaboration module, a dynamic rule update module and a UI module;

[0010] The data acquisition module is used to collect data packets, system logs and device status data in the network environment in real time; the preprocessing and cleaning module is connected to the data acquisition module and is used to preprocess the collected raw data; the intelligent analysis and processing module is connected to the preprocessing and cleaning module and is used to use machine learning, deep learning algorithms combined with big data analysis technology to mine and detect anomalies in the preprocessed multi-source data and identify potential security threats; the zero-trust access control module runs independently of the above modules and is used for fine-grained access control and continuous identity authentication to prevent unauthorized access; the early warning response module is connected to the intelligent analysis and processing module and is used to generate an early warning when the intelligent analysis and processing module detects a security threat. The system generates a report and notifies relevant personnel by email, SMS or system notification, and starts the preset security emergency response measures at the same time; the automated response and isolation module is connected to the early warning response module to automatically execute the preset security response measures; the quantum security encryption module is used to perform quantum cryptography encryption protection on key data; the cloud collaboration module supports unified monitoring and early warning across platforms and regions, and is used for security situation awareness and collaborative defense with intelligent analysis and processing modules in a cloud environment; the dynamic rule update module is connected to the intelligent analysis and processing module to dynamically update the early warning rules and detection logic according to the feedback from the intelligent analysis and processing module and the latest security threat intelligence; the UI module is used to provide an intuitive and easy-to-use user interface.

[0011] As a further preferred embodiment of the present technical solution, the intelligent analysis and processing module includes a data storage unit, an algorithm storage unit and an analysis and processing unit;

[0012] The data storage unit is used to store pre-processed multi-source data, including data packets, system logs, and device status data, for use in mining and anomaly detection;

[0013] The algorithm storage unit is used to store and manage algorithm models and parameters required for machine learning, deep learning algorithms, and big data analysis technologies. The algorithm models include support vector machines, random forests, neural networks for anomaly detection, and clustering algorithms and association rule mining algorithms for pattern recognition;

[0014] The analysis and processing unit uses the algorithm model in the algorithm storage unit to mine and detect anomalies on the data in the data storage unit.

[0015] As a further preferred embodiment of the present technical solution, the intelligent analysis and processing module has self-learning capability and continuously optimizes the algorithm model according to historical data and real-time feedback. The intelligent analysis and processing module supports input of multiple data types and formats, including structured data, semi-structured data and unstructured data.

[0016] Provided as a further preferred embodiment of the present technical solution, the automated response and isolation module includes an intelligent decision-making unit, which is used to automatically select and execute a security response strategy based on the type and severity of the security threat and the current network environment, wherein the security response strategy includes blocking network traffic, isolating infected devices, and starting an emergency backup system.

[0017] As a further preferred embodiment of the present technical solution, the intelligent analysis and processing module also supports the import and export functions of custom algorithm models, allowing users to custom develop algorithm models according to specific needs or scenarios, and import them into the intelligent analysis and processing module, or export existing algorithm models for further analysis and optimization.

[0018] As a further preferred embodiment of the present technical solution, the early warning response module supports a multi-level early warning mechanism, automatically divides the early warning levels according to the severity of the security threat, and triggers corresponding response processes and notification strategies according to different levels.

[0019] As a further preferred embodiment of the present technical solution, the dynamic rule update module is integrated with a third-party security intelligence platform to obtain the latest security threat intelligence and vulnerability information in real time, and automatically analyze and update the warning rules and detection logic.

[0020] In order to solve the above technical problems, another technical solution adopted by the present application is: a method for using a computer network security early warning device, comprising the following steps:

[0021] Step 1: Use the data acquisition module to collect data packets, system logs and device status data in the network environment in real time;

[0022] Step 2: The collected raw data is transmitted to the preprocessing and cleaning module to perform data preprocessing operations;

[0023] Step 3: Input the pre-processed multi-source data into the intelligent analysis and processing module. The intelligent analysis and processing module uses machine learning, deep learning algorithms combined with big data analysis technology to conduct deep mining and anomaly detection on the data to identify potential security threats;

[0024] Step 4: At the same time, the zero-trust access control module runs independently, performing fine-grained access control and continuous identity authentication on all access requests to prevent unauthorized access;

[0025] Step 5: When the intelligent analysis and processing module detects a security threat, the early warning response module immediately generates an early warning report and sends an alert to relevant personnel via email, SMS or system notification, and simultaneously initiates the preset security emergency response measures;

[0026] Step 6: The automated response and isolation module automatically executes the preset security response strategy according to the instructions of the early warning response module, including blocking suspicious network traffic, isolating infected devices, and starting the emergency backup system;

[0027] Step 7: Use quantum security encryption modules to encrypt and protect critical data that needs to be transmitted or stored using quantum cryptography;

[0028] Step 8: The cloud collaboration module supports unified monitoring and early warning across platforms and regions, and works with the intelligent analysis and processing module to enhance security situation awareness. At the same time, the dynamic rule update module obtains the latest security threat intelligence and vulnerability information from the third-party security intelligence platform in real time, and automatically analyzes and updates the early warning rules and detection logic;

[0029] Step 9. The UI module provides an intuitive and easy-to-use user interface, allowing users to view real-time security status, early warning reports, security incident handling progress information, and configure and manage security policies.

[0030] As a further preferred embodiment of the present technical solution, in step 2, the data preprocessing operations include data cleaning, deduplication, formatting, compression and denoising.

[0031] As a further preferred embodiment of the present technical solution, in step three, the intelligent analysis and processing module supports users to customize the configuration of machine learning models and deep learning algorithms according to specific network environments and security requirements to perform anomaly detection and security threat identification.

[0032] In order to solve the above technical problems, another technical solution adopted by this application is:

[0033] A computer network security early warning device comprises a box body, a front surface of which is provided with a protection component, wherein the protection component comprises a cooling fan, a dust cover, a cooling mesh, a first driving motor, a threaded rod, a slider, a second driving motor and a brush;

[0034] A cooling fan is installed on the front surface of the box body, a dust cover is fixedly connected to the front surface of the box body, and cooling mesh holes are evenly opened on the front surface of the dust cover. A first driving motor is symmetrically installed on the top of the dust cover, and an output shaft of the first driving motor is fixedly connected to one end of a threaded rod, a slider is threadedly connected to the outer wall of the threaded rod, a second driving motor is installed on one side of the slider, and an output shaft of the second driving motor is fixedly connected to one end of a brush.

[0035] As a further preferred embodiment of the present technical solution, the threaded rod is rotatably connected to the inner wall of the dust cover, and the brush is rotatably connected between the two sliders.

[0036] Provided as a further preferred embodiment of the present technical solution, the rear surface of the box is symmetrically fixedly connected with two support rods, the tops of the support rods are detachably fixedly connected with storage grooves, the top of the box is provided with an early warning component, the early warning component includes a controller and an audible and visual alarm, the top of the box is installed with a controller, and the top of the controller is installed with an audible and visual alarm.

[0037] As a further preferred embodiment of the present technical solution, a temperature sensor is installed on the inner wall of the box, and the signal output end of the temperature sensor is signal-connected to the signal input end of the controller, and the electrical output end of the controller is electrically connected to the electrical input end of the cooling fan, the first drive motor and the second drive motor.

[0038] The embodiment of the present invention has the following advantages due to the adoption of the above technical solution:

[0039] 1. The present invention introduces intelligent analysis technologies such as machine learning and deep learning, and no longer relies solely on fixed rules and templates to identify attack behaviors. This method can automatically learn and adapt to complex and changeable network attack patterns, improve the flexibility and adaptability of early warning devices, and effectively respond to new and unknown attack methods;

[0040] 2. The present invention significantly enhances the speed and accuracy of data processing and analysis by integrating big data analysis technology with efficient algorithm models, and realizes the real-time processing capability of massive data. It not only ensures that the device can capture subtle changes in the network environment at the first time, but also obtains more comprehensive and detailed network environment information through multi-source data collection and deep mining technology. Combined with intelligent analysis algorithms, the device can accurately identify various types of security threats, whether known or unknown, and can effectively respond, thereby greatly improving the comprehensiveness and accuracy of early warning, and meeting the comprehensive requirements of real-time, accuracy and comprehensiveness;

[0041] 3. Monitor the internal temperature of the box through the temperature sensor. When the temperature exceeds the set value, the cooling fan will speed up the air exchange speed inside and outside the box to ensure the safety of computer network equipment. When the temperature exceeds the threshold, the sound and light alarm will be used to warn the staff to deal with it in time.

[0042] 4. The threaded rod is driven to rotate by the first driving motor, and the brush is driven to rotate by the second driving motor to regularly clean the surface of the dust cover to prevent dust from blocking and reducing the heat dissipation effect.

[0043] The above summary is for illustrative purposes only and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments and features described above, further aspects, embodiments and features of the present invention will be readily apparent by reference to the accompanying drawings and the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0045] Figure 1 A schematic diagram of the functional modules of a computer network security early warning device according to the present invention;

[0046] Figure 2 A schematic diagram of a method for using a computer network security early warning device according to the present invention;

[0047] Figure 3 A structural diagram of a computer network security early warning device according to the present invention;

[0048] Figure 4 This is a box structure diagram of a computer network security early warning device of the present invention;

[0049] Figure 5 A structural diagram of a protection component of a computer network security early warning device according to the present invention;

[0050] Figure 6 This is a structural diagram of an early warning component of a computer network security early warning device of the present invention.

[0051] Figure numerals: 11, housing; 20, protective component; 21, cooling fan; 22, dust cover; 23, cooling mesh; 24, first drive motor; 25, threaded rod; 26, slider; 27, second drive motor; 28, brush; 29, support rod; 210, storage slot; 30, early warning component; 31, controller; 32, sound and light alarm; 33, temperature sensor. DETAILED DESCRIPTION

[0052] The embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.

[0053] It should be clear that the following embodiments of the present disclosure are described by specific specific examples, and those skilled in the art can easily understand other advantages and effects of the present disclosure from the contents disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of the present disclosure, rather than all of the embodiments. The present disclosure can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present disclosure. It should be noted that the following embodiments and features in the embodiments can be combined with each other in the absence of conflict. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present disclosure.

[0054] It should be noted that various aspects of the embodiments within the scope of the appended claims are described below. It should be apparent that the aspects described herein may be embodied in a wide variety of forms, and any specific structure and / or function described herein is merely illustrative. Based on the present disclosure, it should be understood by those skilled in the art that an aspect described herein may be implemented independently of any other aspect, and two or more of these aspects may be combined in various ways. For example, any number of aspects described herein may be used to implement the device and / or practice the method. In addition, other structures and / or functionalities other than one or more of the aspects described herein may be used to implement this device and / or practice this method.

[0055] It should also be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present disclosure. The drawings only show components related to the present disclosure rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

[0056] Additionally, in the following description, specific details are provided to facilitate a thorough understanding of the examples. However, it will be understood by those skilled in the art that the aspects described may be practiced without these specific details.

[0057] Embodiment 1

[0058] Figure 1 Schematic diagram of the functional modules of a computer network security early warning device according to an embodiment of the present application. Figure 1 As shown, a computer network security early warning device includes a data acquisition module, a preprocessing and cleaning module, an intelligent analysis and processing module, a zero-trust access control module, an early warning response module, an automated response and isolation module, a quantum security encryption module, a cloud collaboration module, a dynamic rule update module and a UI module;

[0059] The data acquisition module is used to collect data packets, system logs and device status data in the network environment in real time; the preprocessing and cleaning module is connected to the data acquisition module to preprocess the collected raw data; the intelligent analysis and processing module is connected to the preprocessing and cleaning module to use machine learning, deep learning algorithms combined with big data analysis technology to mine and detect anomalies in the preprocessed multi-source data and identify potential security threats; the zero-trust access control module runs independently of the above modules and is used for fine-grained access control and continuous identity authentication to prevent unauthorized access; the early warning response module is connected to the intelligent analysis and processing module to generate an early warning report when the intelligent analysis and processing module detects a security threat , and notify relevant personnel through email, SMS or system notification, and start the preset security emergency response measures at the same time; the automated response and isolation module is connected to the early warning response module to automatically execute the preset security response measures; the quantum security encryption module is used to encrypt and protect key data with quantum cryptography; the cloud collaboration module supports cross-platform and cross-regional unified monitoring and early warning, and is used for security situation awareness and intelligent analysis and processing module collaborative defense in the cloud environment; the dynamic rule update module is connected to the intelligent analysis and processing module to dynamically update the early warning rules and detection logic according to the feedback from the intelligent analysis and processing module and the latest security threat intelligence; the UI module is used to provide an intuitive and easy-to-use user interface; among them,

[0060] The data collection module uses technical means such as network packet capture tools, log collectors and equipment status monitoring interfaces to ensure the comprehensiveness and real-time nature of the data; the preprocessing and cleaning module uses data cleaning algorithms and distributed processing frameworks to quickly process massive amounts of data and store them in an intermediate database for subsequent analysis; the zero-trust access control module uses technical means such as multi-factor authentication and role-based access control strategies to ensure system security; the quantum security encryption module uses quantum cryptography technologies such as quantum key distribution and quantum random number generation to ensure the confidentiality and integrity of data transmission; the cloud collaboration module uses the resource pooling, elastic expansion and on-demand service characteristics of the cloud computing platform to achieve real-time sharing and collaborative processing of security information; the dynamic rule update module is integrated with a third-party security intelligence platform to obtain the latest security threat intelligence and vulnerability information in real time; the UI module uses a graphical interface design to support multi-terminal access and customized layout.

[0061] In one embodiment, specifically, the intelligent analysis and processing module includes a data storage unit, an algorithm storage unit, and an analysis and processing unit;

[0062] The data storage unit is used to store pre-processed multi-source data, including data packets, system logs, and device status data, for mining and anomaly detection. The data storage unit uses efficient data storage technology and optimization strategies to ensure that data can be quickly read and written to support subsequent deep mining and anomaly detection operations. At the same time, the unit also has data backup and recovery functions to prevent data loss or damage.

[0063] The algorithm storage unit is used to store and manage the algorithm models and parameters required for machine learning, deep learning algorithms and big data analysis technologies. The algorithm models include support vector machines, random forests, neural networks for anomaly detection, and clustering algorithms and association rule mining algorithms for pattern recognition; the algorithm models stored in the algorithm storage unit include but are not limited to: anomaly detection algorithms, such as support vector machines (SVM), random forests, neural networks, etc. These algorithms can identify abnormal patterns in data and thus discover potential security threats; pattern recognition algorithms, such as clustering algorithms (K-means, DBSCAN, etc.) and association rule mining algorithms (Apriori, FP-Growth, etc.), which can discover hidden patterns and associations in data, and further assist in anomaly detection and security analysis.

[0064] The analysis and processing unit uses the algorithm model in the algorithm storage unit to perform deep mining and anomaly detection on the data in the data storage unit; the workflow of the analysis and processing unit is as follows:

[0065] Load the data set to be analyzed from the data storage unit; select a suitable algorithm model from the algorithm storage unit according to the analysis requirements and data characteristics; configure the parameters and configuration information of the algorithm model to ensure that the model can accurately analyze the data; for the algorithm model that needs to be trained (such as neural network), use part of the data set for training to optimize the model performance; use the selected algorithm model to perform deep mining and anomaly detection on the data set to identify potential security threats; output the analysis results to subsequent modules (such as early warning response module) so that corresponding security measures can be taken.

[0066] In one embodiment, specifically, the intelligent analysis and processing module has self-learning capabilities, and continuously optimizes the algorithm model based on historical data and real-time feedback. The intelligent analysis and processing module supports input of multiple data types and formats, including structured data, semi-structured data, and unstructured data. The intelligent analysis and processing module will review historical data regularly or on demand, and analyze the patterns, trends, and anomalies in these data. Through these analyses, the module can identify which algorithm models perform best in specific scenarios and which parameter settings are most effective. In addition to historical data, the module will also receive real-time feedback from other parts of the system, such as the processing results of the early warning response module, the user's manual intervention records, etc. These real-time feedbacks provide the module with direct evidence of its analysis accuracy, which helps the module to quickly adjust the algorithm model to adapt to new security threats or data features. Based on historical data and real-time feedback, the module will automatically or under the guidance of the administrator optimize the algorithm model, which may include adjusting model parameters, replacing algorithm models that are more suitable for the current scenario, or even integrating multiple models to form a more powerful analysis engine; structured data includes tabular data in the database, which usually has fixed fields and a clear format. The module can easily read this type of data and use its structured characteristics for efficient analysis; semi-structured data includes data in formats such as JSON and XML. They have a certain structure but are not as strict as structured data. The intelligent analysis and processing module can parse the structure of this type of data and extract useful information for analysis; unstructured data includes text, images, audio, etc., which have no fixed format and structure. For this type of data, the intelligent analysis and processing module will use natural language processing, image recognition, audio analysis and other technologies for preprocessing and feature extraction for subsequent analysis and processing.

[0067] In one embodiment, specifically, the automated response and isolation module includes an intelligent decision-making unit, which is used to automatically select and execute security response strategies according to the type and severity of security threats and the current network environment. The security response strategies include blocking network traffic, isolating infected devices, and starting emergency backup systems. The intelligent decision-making unit is the brain of the automated response and isolation module. It integrates advanced analysis algorithms and decision-making logic, and can process information from multiple sources such as security monitoring and intelligent analysis and processing modules in real time, and make fast and accurate decisions accordingly: First, the intelligent decision-making unit will conduct a comprehensive assessment of the detected security threats, including identifying the type of threats (such as viruses, Trojans, DDoS attacks, etc.), assessing their severity, and After completing the threat assessment, the intelligent decision-making unit will automatically select the most suitable security response strategy for the current situation based on the preset decision logic and policy library, and will analyze the severity of the threat (such as the scope of impact, potential losses, etc.) and the current network environment (such as network topology, device status, traffic distribution, etc.). These strategies include but are not limited to blocking network traffic (to prevent the threat from spreading further), isolating infected devices (to prevent them from continuing to harm the network), and starting the emergency backup system (to ensure business continuity). Once the security response strategy is determined, the intelligent decision-making unit will immediately trigger the corresponding execution mechanism to ensure that the strategy is implemented in a timely and effective manner, including sending control instructions to network devices, starting or shutting down specific security services, adjusting network configurations, etc.

[0068] In one embodiment, specifically, the intelligent analysis and processing module also supports the import and export functions of custom algorithm models, allowing users to customize and develop algorithm models according to specific needs or scenarios, and import them into the intelligent analysis and processing module, or export existing algorithm models for further analysis and optimization. The intelligent analysis and processing module provides a variety of export formats for users to choose from to meet the needs of different scenarios. These formats include standard machine learning model files (such as PMML, ONNX, etc.), source code files (such as Python scripts, C++ codes, etc.) or model files of specific platforms (such as TensorFlow's SavedModel, PyTorch's TorchScript, etc.). By supporting the import and export functions of custom algorithm models, users are provided with more flexible and personalized services. This function not only helps users customize algorithm models according to their own needs and improve the accuracy and efficiency of analysis, but also helps promote the sharing and exchange of algorithm models, and promotes the continuous development and progress of network security analysis technology.

[0069] In one embodiment, specifically, the early warning response module supports a multi-level early warning mechanism, automatically divides the early warning level according to the severity of the security threat, and triggers the corresponding response process and notification strategy according to different levels. The early warning response module will first conduct a comprehensive assessment of the detected security threat, including the type of threat, the scope of impact, potential losses and other dimensions. Based on these assessment results, the module will calculate a threat severity index. According to the threat severity index, the early warning response module will automatically divide the security threat into different early warning levels, which usually include low, medium, high and emergency levels. Each level corresponds to a different response priority and notification strategy. Once the early warning level is determined, the early warning response module will immediately trigger the response process corresponding to the level. These processes include automatically blocking network traffic, isolating infected devices, starting the emergency backup system, notifying the security team, etc. In addition to triggering the response process, the early warning response module will also execute the corresponding notification strategy according to the early warning level, which includes sending alarm notifications to the security team, sending emergency notifications to key personnel via email or SMS, and displaying emergency information on the monitoring screen. Different levels of early warnings will trigger different levels of notification strategies to ensure that relevant personnel can obtain important information in a timely manner and take corresponding measures.

[0070] In one embodiment, specifically, the dynamic rule update module is integrated with a third-party security intelligence platform to obtain security threat intelligence and vulnerability information in real time, automatically analyze and update warning rules and detection logic. Through real-time integration with a third-party security intelligence platform, the module can quickly obtain the latest security threat information and ensure the timeliness and effectiveness of warning and detection rules.

[0071] Embodiment 2

[0072] Figure 2 1 is a flowchart of a method for using a computer network security early warning device according to an embodiment of the present invention. It should be noted that if there are substantially the same results, the method of the present application is not limited to Figure 1 The process sequence shown is limited. Figure 2 As shown: A method for using a computer network security early warning device, comprising the following steps:

[0073] Step 1: Use the data acquisition module to collect data packets, system logs, and device status data in the network environment in real time. These data are the basis for subsequent analysis and early warning;

[0074] Step 2: The collected raw data is sent to the preprocessing and cleaning module to perform data preprocessing operations, which are aimed at improving data quality and reducing noise and errors in subsequent analysis;

[0075] Step 3: Input the pre-processed multi-source data into the intelligent analysis and processing module. The intelligent analysis and processing module uses machine learning, deep learning algorithms and big data analysis technology to conduct deep mining and anomaly detection on the data to identify potential security threats. The key to this step is to identify potential security threats.

[0076] Step 4: At the same time, the zero-trust access control module runs independently, performs fine-grained access control and continuous identity authentication on all access requests to prevent unauthorized access and ensure that only authorized entities can access network resources, thereby effectively preventing unauthorized access;

[0077] Step 5: When the intelligent analysis and processing module detects a security threat, the early warning response module immediately generates an early warning report and sends an alert to relevant personnel via email, SMS or system notification, and simultaneously initiates the preset security emergency response measures to quickly respond to the threat;

[0078] Step 6: The automated response and isolation module automatically executes the preset security response strategy according to the instructions of the early warning response module, including blocking suspicious network traffic, isolating infected devices and starting the emergency backup system to reduce the impact of threats on the network system;

[0079] Step 7: Use quantum security encryption modules to encrypt and protect critical data that needs to be transmitted or stored. Quantum encryption technology provides higher security than traditional encryption methods and can resist the cracking of encrypted information by quantum computers.

[0080] Step 8: The cloud collaboration module supports unified monitoring and early warning across platforms and regions, and works with the intelligent analysis and processing module to enhance security situation awareness. At the same time, the dynamic rule update module obtains the latest security threat intelligence and vulnerability information from the third-party security intelligence platform in real time, automatically analyzes and updates early warning rules and detection logic, and ensures that the system can respond to emerging threats in a timely manner;

[0081] Step 9. The UI module provides an intuitive and easy-to-use user interface, allowing users to view real-time security status, early warning reports, security incident handling progress information, and configure and manage security policies. This interactivity allows users to participate more actively in network security management.

[0082] In one embodiment, specifically, in step two, the data preprocessing operations include data cleaning, deduplication, formatting, compression and denoising, wherein data cleaning is used to remove incomplete, erroneous or duplicate data; deduplication is used to ensure that there are no duplicate records in the data set; formatting is used to convert the data into a unified and standardized format for subsequent processing; compression is used to reduce data storage space while maintaining data integrity; and denoising is used to remove irrelevant or misleading information from the data.

[0083] In one embodiment, specifically, in step three, the intelligent analysis and processing module supports users to customize the configuration of machine learning models and deep learning algorithms according to specific network environments and security requirements to perform anomaly detection and security threat identification. First, users need to clarify their network environment and the types of security threats they face, which includes understanding the network architecture, traffic patterns, common attack methods, and the location of sensitive data. Based on the results of the demand analysis, users can select a suitable model from a predefined machine learning model library as a starting point. These models may include different algorithms for classification, clustering, anomaly detection, etc. Users can adjust the parameters of the selected model to optimize its performance, which includes the setting of hyperparameters such as learning rate, number of iterations, regularization parameters, and the adjustment of data preprocessing steps such as feature selection and feature scaling. In addition to a single model, users You can also choose to integrate multiple algorithms to build a more complex model structure. For example, you can use integrated learning methods (such as Bagging and Boosting) to improve the accuracy and stability of the model. If the predefined model cannot meet specific needs, users can also write custom machine learning or deep learning algorithms, which requires users to have certain programming and algorithm design capabilities. After completing the model configuration, users need to train the model using historical data and evaluate its performance through methods such as cross-validation, which helps ensure the effectiveness and reliability of the model in practical applications. The trained model will be deployed to the intelligent analysis and processing module for real-time analysis of network data and identification of potential security threats. At the same time, users need to continuously monitor the performance of the model and adjust the configuration or retrain the model as needed to adapt to new threat types.

[0084] Embodiment 3

[0085] This embodiment provides an enterprise-level network security early warning solution, and the specific implementation methods are as follows:

[0086] Deploy data collection modules at key nodes at the network boundary (such as firewalls, intrusion detection systems), key servers (such as database servers, Web servers), and important application systems to capture network traffic packets, system logs (such as operating system logs, application logs), and device status information (such as CPU usage, memory usage, disk space, etc.) in real time, and use encrypted transmission protocols (such as TLS / SSL) to transmit the collected data to ensure the security of data during transmission;

[0087] The preprocessing and cleaning module integrates advanced data cleaning algorithms, such as hash-based duplicate data detection, rule-based invalid data filtering, and statistical noise data elimination, to uniformly format data from different sources and formats for the convenience of subsequent analysis and processing modules, and uses a distributed storage system (such as Hadoop HDFS) to store cleaned data to ensure data scalability and fault tolerance;

[0088] The intelligent analysis and processing module uses customized deep learning models, such as convolutional neural networks (CNNs), to identify malicious traffic in network traffic. At the same time, it uses recurrent neural networks (RNNs) to detect abnormal behaviors in log sequences, and combines big data analysis frameworks (such as Apache Spark) to quickly process and analyze massive data to discover potential threat patterns and abnormal behaviors. At the same time, it integrates third-party threat intelligence data into the analysis process to improve the accuracy and timeliness of threat detection.

[0089] The zero-trust access control module implements role-based access control strategies to ensure that users can only access resources within their authority, and adopts a multi-factor authentication mechanism to enhance the security of user identities. At the same time, it continuously monitors and evaluates user behavior to promptly detect and prevent potential security threats.

[0090] The early warning response module and the automated response and isolation module are seamlessly integrated with network firewalls, intrusion prevention systems (IPS), terminal security management software and other security devices. When suspicious traffic or malicious behavior is detected, the blocking mechanism is automatically triggered to prevent the spread of threats. For infected devices or systems, they are automatically isolated to prevent them from continuing to harm the network environment.

[0091] The cloud collaboration module uses the cloud platform to achieve unified monitoring and early warning on a global scale, and supports cross-regional security situation awareness, real-time understanding of the security status of branches in various regions, and realizes data sharing and collaborative analysis on the cloud to enhance overall security defense capabilities;

[0092] The dynamic rule update module obtains the latest threat intelligence and vulnerability information in real time, and automatically updates the security rule base and detection model based on the acquired intelligence information;

[0093] The UI module provides an intuitive and easy-to-use enterprise-level user interface that displays network security status, threat alerts, event handling progress and other information in real time. It also supports multi-user and multi-role access, making it convenient for security teams and management to configure and adjust policies.

[0094] The enterprise-level network security early warning solution of this embodiment is suitable for large enterprises, especially those with multiple departments, branches and a large number of remote office users. It can effectively monitor and warn of various network security threats from inside and outside, including but not limited to network attacks, malware, data leakage, etc., and realize comprehensive monitoring and early warning of the enterprise network environment. The system can not only detect and block potential network threats in a timely manner, but also continuously improve security defense capabilities through functions such as cloud collaboration and dynamic rule updates, thereby ensuring the safe operation of enterprise business.

[0095] Embodiment 4

[0096] This embodiment provides a financial industry network security early warning solution, the specific implementation is as follows:

[0097] Deploy efficient data collection modules, covering key nodes such as transaction systems, user behavior log servers, and payment processing centers, and use streaming processing technology to capture key data such as transaction records, login activities, and payment requests in real time;

[0098] The collected data is desensitized through the preprocessing and cleaning modules, such as using hash algorithms to encrypt sensitive information such as user ID numbers and bank card numbers. At the same time, invalid or duplicate data is removed to reduce the burden of subsequent analysis.

[0099] The intelligent analysis and processing module uses the SVM algorithm to analyze transaction behaviors and identify abnormal transaction features, such as large-amount abnormal transfers and multiple transactions in a short period of time;

[0100] The zero-trust access control module implements access control strategies based on the zero-trust principle, and performs identity authentication, permission verification, and behavior auditing on all access requests. Security measures such as IP whitelists and time window restrictions are set to prevent unauthorized access;

[0101] The early warning response module and the automated response and isolation module integrate the real-time transaction monitoring system. Once suspicious transactions or abnormal behaviors are detected, the automatic response mechanism is immediately triggered, such as blocking transactions, freezing accounts, or notifying the risk control team for manual review;

[0102] The quantum security encryption module uses quantum encryption technology to protect transaction data and sensitive information to prevent hackers from cracking and stealing;

[0103] Build a cloud collaboration platform through the cloud collaboration module to achieve data synchronization and early warning information sharing among multiple data centers, and improve the overall protection capability of the system;

[0104] The dynamic rule update module establishes a cooperative relationship with the authoritative security intelligence platform to regularly obtain the latest fraud methods and vulnerability information, and dynamically update it to the intelligent analysis and processing module to maintain a keen insight into security threats and rapid response capabilities;

[0105] The UI module provides a customized financial industry interface that supports security teams to monitor security events in real time, query historical records, generate detailed reports, and configure policies.

[0106] This embodiment provides a comprehensive security warning solution for the financial industry by building a multi-level and multi-dimensional security protection system. From data collection, preprocessing, intelligent analysis to automated response, and then to cloud collaboration and dynamic updates, each link is closely linked to jointly build an indestructible security line to ensure the stable operation of financial services and the security of customer data.

[0107] To sum up, the computer network security early warning device provided by the present invention has the advantages of comprehensive data collection capabilities, efficient data processing and cleaning, intelligent analysis and threat detection, powerful access control and identity authentication, automated response and isolation, cloud collaboration and dynamic update, and intuitive and easy-to-use user interface. It shows a high degree of flexibility and adaptability, which can not only meet the complex needs of enterprise-level network security early warning, but also can be effectively applied to the field of network security early warning in the financial industry.

[0108] Embodiment 5

[0109] like Figure 3-Figure 6 As shown, an embodiment of the present invention provides a computer network security early warning device, including a box body 11, a protective component 20 is provided on the front surface of the box body 11, and the protective component 20 includes a cooling fan 21, a dust cover 22, a cooling mesh 23, a first driving motor 24, a threaded rod 25, a slider 26, a second driving motor 27 and a brush 28;

[0110] A cooling fan 21 is installed on the front surface of the box body 11, and a dust cover 22 is fixedly connected to the front surface of the box body 11. The front surface of the dust cover 22 is evenly provided with cooling mesh holes 23. A first driving motor 24 is symmetrically installed on the top of the dust cover 22. The output shaft of the first driving motor 24 is fixedly connected to one end of a threaded rod 25. A slider 26 is threadedly connected to the outer wall of the threaded rod 25. A second driving motor 27 is installed on one side of the slider 26. The output shaft of the second driving motor 27 is fixedly connected to one end of a brush 28. The threaded rod 25 is driven to rotate by the first driving motor 24, and the slider 26 and the brush 28 are driven to move up and down. At the same time, the brush 28 is driven to rotate by the second driving motor 27 to regularly clean the surface of the dust cover 22.

[0111] In this embodiment, specifically: the threaded rod 25 is rotatably connected to the inner wall of the dust cover 22, the threaded rod 25 is driven to rotate by the first drive motor 24, the brush 28 is rotatably connected between the two sliders 26, and the surface of the dust cover 22 is regularly cleaned by the brush 28.

[0112] In this embodiment, specifically: two support rods 29 are symmetrically fixedly connected to the rear surface of the box body 11, and a buckle is set on the top of the support rod 29 for connecting to the storage groove 210. The top of the support rod 29 is detachably fixedly connected with the storage groove 210. Dust is collected through the storage groove 210 for unified treatment. An early warning component 30 is set on the top of the box body 11. The early warning component 30 includes a controller 31 and an audible and visual alarm 32. The controller 31 is installed on the top of the box body 11, and the audible and visual alarm 32 is installed on the top of the controller 31. When the internal temperature of the box body 11 exceeds the threshold value, the audible and visual alarm 32 is used to issue an early warning.

[0113] In this embodiment, specifically: a temperature sensor 33 is installed on the inner wall of the box body 11, and the signal output end of the temperature sensor 33 is signal-connected to the signal input end of the controller 31, and the internal temperature of the box body 11 is monitored by the temperature sensor 33, and the electrical output end of the controller 31 is electrically connected to the electrical input ends of the cooling fan 21, the first drive motor 24 and the second drive motor 27, and the early warning device is controlled to work through the controller 31.

[0114] When the present invention is working: the internal temperature of the box body 11 is monitored by the temperature sensor 33. When the temperature exceeds the set value, the cooling fan 21 is operated to accelerate the air exchange speed inside and outside the box body 11, thereby ensuring the safety of computer network equipment. When the temperature exceeds the threshold, the sound and light alarm 32 is used for early warning to remind the staff to deal with it in time. The first driving motor 24 drives the threaded rod 25 to rotate, drives the slider 26 and the brush 28 to move up and down, and at the same time uses the second driving motor 27 to drive the brush 28 to rotate, and the surface of the dust cover 22 is regularly cleaned to prevent dust from blocking and reducing the heat dissipation effect. The dust is collected through the storage groove 210 for unified treatment.

[0115] The basic principles of the present disclosure are described above in conjunction with specific embodiments. However, it should be noted that the advantages, strengths, effects, etc. mentioned in the present disclosure are only examples and not limitations, and it cannot be considered that these advantages, strengths, effects, etc. are required by each embodiment of the present disclosure. In addition, the specific details disclosed above are only for the purpose of illustration and ease of understanding, and are not limitations. The above details do not limit the present disclosure to the necessity of adopting the above specific details to be implemented.

[0116] In the present disclosure, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply that there is any such actual relationship or order between these entities or operations. The block diagrams of the devices, devices, equipment, and systems involved in the present disclosure are only illustrative examples and are not intended to require or imply that they must be connected, arranged, and configured in the manner shown in the block diagram. As will be appreciated by those skilled in the art, these devices, devices, equipment, and systems can be connected, arranged, and configured in any manner. Words such as "including", "comprising", "having", etc. are open words, referring to "including but not limited to", and can be used interchangeably with them. The words "or" and "and" used here refer to the words "and / or" and can be used interchangeably with them, unless the context clearly indicates otherwise. The words "such as" used here refer to the phrase "such as but not limited to", and can be used interchangeably with them.

[0117] Additionally, as used herein, "or" used in a list of items beginning with "at least one" indicates a separate list, so that, for example, a list of "at least one of A, B, or C" means A or B or C, or AB or AC or BC, or ABC (i.e., A and B and C). Furthermore, the word "exemplary" does not mean that the example described is preferred or better than other examples.

[0118] It should also be noted that in the system and method of the present disclosure, each component or each step can be decomposed and / or recombined. Such decomposition and / or recombination should be regarded as equivalent solutions of the present disclosure.

[0119] Various changes, substitutions, and modifications of the techniques described herein may be made without departing from the teachings defined by the appended claims. Furthermore, the scope of the claims of the present disclosure is not limited to the specific aspects of the processes, machines, manufactures, compositions of events, means, methods, and actions described above. Currently existing or later to be developed processes, machines, manufactures, compositions of events, means, methods, or actions that perform substantially the same functions or achieve substantially the same results as the corresponding aspects described herein may be utilized. Thus, the appended claims include such processes, machines, manufactures, compositions of events, means, methods, or actions within their scope.

[0120] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present disclosure. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of the present disclosure. Therefore, the present disclosure is not intended to be limited to the aspects shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

[0121] The above description has been given for the purpose of illustration and description. In addition, this description is not intended to limit the embodiments of the present disclosure to the forms disclosed herein. Although multiple example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, changes, additions and sub-combinations thereof.

Claims

1. A computer network security early warning device, characterized in that: It includes data acquisition module, preprocessing and cleaning module, intelligent analysis and processing module, zero-trust access control module, early warning response module, automated response and isolation module, quantum security encryption module, cloud collaboration module, dynamic rule update module and UI module; The data acquisition module is used to collect data packets, system logs and device status data in the network environment in real time; the preprocessing and cleaning module is connected to the data acquisition module and is used to preprocess the collected raw data; The intelligent analysis and processing module is connected to the preprocessing and cleaning module, and is used to mine and detect anomalies in the preprocessed multi-source data using machine learning and deep learning algorithms combined with big data analysis technology to identify potential security threats; The zero-trust access control module operates independently of the above modules and is used for fine-grained access control and continuous identity authentication to prevent unauthorized access; The early warning response module is connected to the intelligent analysis and processing module, and is used to generate an early warning report when the intelligent analysis and processing module detects a security threat, and notify relevant personnel by email, SMS or system notification, and initiate preset security emergency response measures; The automated response and isolation module is connected to the early warning response module to automatically execute preset safety response measures; The quantum security encryption module is used to perform quantum cryptography encryption protection on key data; the cloud collaboration module supports cross-platform and cross-regional unified monitoring and early warning, and is used for security situation awareness and intelligent analysis and processing module collaborative defense in the cloud environment; The dynamic rule update module is connected to the intelligent analysis and processing module, and is used to dynamically update the warning rules and detection logic according to the feedback of the intelligent analysis and processing module and the latest security threat intelligence; the UI module is used to provide an intuitive and easy-to-use user interface; the intelligent analysis and processing module includes a data storage unit, an algorithm storage unit and an analysis and processing unit; The data storage unit is used to store pre-processed multi-source data, including data packets, system logs, and device status data, for use in mining and anomaly detection; The algorithm storage unit is used to store and manage algorithm models and parameters required for machine learning, deep learning algorithms, and big data analysis technologies. The algorithm models include support vector machines, random forests, neural networks for anomaly detection, and clustering algorithms and association rule mining algorithms for pattern recognition; The analysis and processing unit utilizes the algorithm model in the algorithm storage unit to mine and detect anomalies in the data in the data storage unit; the intelligent analysis and processing module has self-learning capabilities and continuously optimizes the algorithm model based on historical data and real-time feedback. The intelligent analysis and processing module supports input of multiple data types and formats, including structured data, semi-structured data, and unstructured data.

2. A computer network security early warning device according to claim 1, characterized in that: The automated response and isolation module includes an intelligent decision-making unit, which is used to automatically select and execute security response strategies based on the type and severity of security threats and the current network environment. The security response strategies include blocking network traffic, isolating infected devices, and starting emergency backup systems. The intelligent analysis and processing module also supports the import and export functions of custom algorithm models, allowing users to customize the development of algorithm models based on specific needs or scenarios, and import them into the intelligent analysis and processing module, or export existing algorithm models for further analysis and optimization. The early warning response module supports a multi-level early warning mechanism, automatically divides the warning level according to the severity of the security threat, and triggers corresponding response processes and notification strategies based on different levels.

3. A computer network security early warning device according to claim 1, characterized in that: The dynamic rule update module is integrated with a third-party security intelligence platform to obtain security threat intelligence and vulnerability information in real time, and automatically analyze and update warning rules and detection logic.

4. A computer network security early warning device, comprising a box (11), characterized in that: A protection assembly (20) is provided on the front surface of the box body (11), and the protection assembly (20) comprises a cooling fan (21), a dust cover (22), a cooling mesh (23), a first driving motor (24), a threaded rod (25), a slider (26), a second driving motor (27) and a brush (28); A cooling fan (21) is installed on the front surface of the box body (11), a dust cover (22) is fixedly connected to the front surface of the box body (11), and cooling mesh holes (23) are evenly opened on the front surface of the dust cover (22). A first driving motor (24) is symmetrically installed on the top of the dust cover (22), an output shaft of the first driving motor (24) is fixedly connected to one end of a threaded rod (25), an outer wall of the threaded rod (25) is threadedly connected to a slider (26), a second driving motor (27) is installed on one side of the slider (26), and an output shaft of the second driving motor (27) is fixedly connected to one end of a brush (28).

5. A computer network security early warning device according to claim 4, characterized in that: The threaded rod (25) is rotatably connected to the inner wall of the dust cover (22); and the brush (28) is rotatably connected between the two sliders (26).

6. A computer network security early warning device according to claim 4, characterized in that: The rear surface of the box (11) is symmetrically fixedly connected to two support rods (29); the top of the support rod (29) is detachably fixedly connected to a storage groove (210); the top of the box (11) is provided with an early warning component (30), the early warning component (30) comprises a controller (31) and an audible and visual alarm (32); the top of the box (11) is installed with the controller (31), and the top of the controller (31) is installed with the audible and visual alarm (32).

7. A computer network security early warning device according to claim 6, characterized in that: A temperature sensor (33) is installed on the inner wall of the box body (11), and the signal output end of the temperature sensor (33) is signal-connected to the signal input end of the controller (31); the electrical output end of the controller (31) is electrically connected to the electrical input ends of the cooling fan (21), the first drive motor (24) and the second drive motor (27).

8. A method for using a computer network security early warning device, applied to a computer network security early warning device as claimed in any one of claims 1 to 3, characterized in that: The following steps are involved: Step 1: Use the data acquisition module to collect data packets, system logs and device status data in the network environment in real time; Step 2: The collected raw data is transmitted to the preprocessing and cleaning module to perform data preprocessing operations; Step 3: Input the pre-processed multi-source data into the intelligent analysis and processing module. The intelligent analysis and processing module uses machine learning, deep learning algorithms combined with big data analysis technology to conduct deep mining and anomaly detection on the data to identify potential security threats; Step 4: At the same time, the zero-trust access control module runs independently, performing fine-grained access control and continuous identity authentication on all access requests to prevent unauthorized access; Step 5: When the intelligent analysis and processing module detects a security threat, the early warning response module immediately generates an early warning report and sends an alert to relevant personnel via email, SMS or system notification, and simultaneously initiates the preset security emergency response measures; Step 6: The automated response and isolation module automatically executes the preset security response strategy according to the instructions of the early warning response module, including blocking suspicious network traffic, isolating infected devices, and starting the emergency backup system; Step 7: Use quantum security encryption modules to encrypt and protect critical data that needs to be transmitted or stored using quantum cryptography; Step 8: The cloud collaboration module supports unified monitoring and early warning across platforms and regions, and works in collaboration with the intelligent analysis and processing module. At the same time, the dynamic rule update module obtains security threat intelligence and vulnerability information from the third-party security intelligence platform in real time, and automatically analyzes and updates early warning rules and detection logic; Step 9. The UI module provides an intuitive and easy-to-use user interface, allowing users to view real-time security status, early warning reports, security incident handling progress information, and configure and manage security policies.

9. The method for using a computer network security early warning device according to claim 8, characterized in that: In step 2, the data preprocessing operations include data cleaning, deduplication, formatting, compression and denoising.

10. The method for using a computer network security early warning device according to claim 8, characterized in that: In step three, the intelligent analysis and processing module supports users to customize the configuration of machine learning models and deep learning algorithms according to specific network environments and security requirements to perform anomaly detection and security threat identification.

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